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Mike Morgan edited this page Jan 11, 2026 · 1 revision

Technical Specifications

Detailed technical documentation for Cortex Linux system architecture, performance, and specifications.

Table of Contents

  1. System Architecture
  2. Performance Benchmarks
  3. Hardware Requirements
  4. Software Stack
  5. Kernel Enhancements
  6. AI Engine Specifications
  7. Performance Tuning
  8. Scalability

System Architecture

High-Level Architecture

┌─────────────────────────────────────────────────────────┐
│                    Application Layer                     │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐ │
│  │   CLI Tools  │  │  HTTP API    │  │  Libraries   │ │
│  └──────────────┘  └──────────────┘  └──────────────┘ │
└─────────────────────────────────────────────────────────┘
                          │
┌─────────────────────────────────────────────────────────┐
│                     Service Layer                        │
│  ┌──────────────────────────────────────────────────┐  │
│  │         systemd Services                         │  │
│  │  - cortex-ai.service (HTTP API server)          │  │
│  │  - cortex-scheduler.service (AI task queue)     │  │
│  │  - cortex-monitor.service (system monitoring)   │  │
│  └──────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────┘
                          │
┌─────────────────────────────────────────────────────────┐
│                      AI Layer                            │
│  ┌──────────────────────────────────────────────────┐  │
│  │         Sapiens 0.27B Engine                     │  │
│  │  - Model: 270M parameters                       │  │
│  │  - Runtime: Custom C++ inference engine         │  │
│  │  - Memory: ~200MB                                │  │
│  │  - API: C API for system integration             │  │
│  └──────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────┘
                          │
┌─────────────────────────────────────────────────────────┐
│                    Kernel Layer                          │
│  ┌──────────────────────────────────────────────────┐  │
│  │  Linux Kernel 6.1+ (Cortex-enhanced)            │  │
│  │  - AI-aware process scheduler                    │  │
│  │  - Enhanced memory management                    │  │
│  │  - Real-time capabilities                        │  │
│  │  - Resource isolation                            │  │
│  └──────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────┘

Component Details

Kernel Layer

Base: Linux kernel 6.1.0+

Enhancements:

  • AI-Aware Scheduler: Optimizes CPU allocation for AI workloads
  • Memory Management: Efficient handling of large model memory requirements
  • I/O Optimization: Reduced latency for model loading and inference
  • Resource Isolation: CPU and memory isolation for AI processes

Kernel Modules:

  • cortex_scheduler.ko: AI workload scheduling
  • cortex_memory.ko: Enhanced memory management
  • cortex_monitor.ko: System metrics collection

Configuration:

# Kernel configuration options
CONFIG_CORTEX_AI_SCHEDULER=y
CONFIG_CORTEX_MEMORY_MANAGEMENT=y
CONFIG_CORTEX_MONITOR=y
CONFIG_CORTEX_RT_CAPABILITIES=y

AI Layer

Engine: Sapiens 0.27B Reasoning Engine

Specifications:

  • Model Size: 270 million parameters
  • Model Format: Quantized INT8 (optimized for inference)
  • Memory Usage: ~200MB RAM
  • Disk Size: ~350MB (compressed model)
  • Inference Engine: Custom C++ implementation
  • Supported Operations: Reasoning, planning, debugging, optimization

Model Architecture:

  • Type: Transformer-based language model
  • Layers: 24 transformer layers
  • Attention Heads: 16
  • Hidden Size: 1024
  • Vocabulary Size: 50,000 tokens
  • Context Length: 2048 tokens

Performance Characteristics:

  • Inference Latency: 50-200ms (typical)
  • Throughput: 5-10 queries/second (single-threaded)
  • Concurrent Requests: Up to 50 (with queuing)
  • Memory Efficiency: Optimized for low-memory environments

Service Layer

HTTP API Server:

  • Framework: Go-based HTTP server
  • Port: 8080 (configurable)
  • Protocol: HTTP/1.1, HTTP/2
  • Endpoints: /reason, /plan, /debug, /optimize, /health, /status
  • Authentication: Optional API key
  • Rate Limiting: Configurable per-endpoint

CLI Tool:

  • Language: Rust
  • Binary: /usr/bin/cortex-ai
  • Commands: reason, plan, debug, optimize, status, version
  • Output Formats: Text, JSON, Markdown

Systemd Services:

  • cortex-ai.service: Main AI service
  • cortex-scheduler.service: Task scheduling
  • cortex-monitor.service: System monitoring

Application Layer

Base System: Debian/Ubuntu-compatible userland

Package Management: APT (Advanced Package Tool)

Development Tools:

  • GCC 11+, Clang 14+
  • Python 3.10+
  • Rust 1.70+
  • Go 1.20+

Standard Libraries:

  • glibc 2.35+
  • OpenSSL 3.0+
  • zlib, bzip2, xz

Performance Benchmarks

Inference Performance

Latency Benchmarks

Test environment: 4-core Intel i7-8700K, 16GB RAM, SSD

Query Complexity P50 Latency P95 Latency P99 Latency
Simple (1-50 tokens) 67ms 120ms 180ms
Medium (51-200 tokens) 145ms 280ms 420ms
Complex (201-500 tokens) 234ms 450ms 680ms
Very Complex (501-1000 tokens) 380ms 720ms 1100ms

Throughput Benchmarks

Configuration Queries/Second Concurrent Requests
Single-threaded 6.2 1
4 threads 18.5 4
8 threads 28.3 8
With batching (batch=10) 45.2 50

Accuracy Benchmarks

Task Performance

Task Accuracy Notes
Sudoku Solving 55% On-device, no API calls
Code Debugging 72% Python, JavaScript, Bash
Architecture Planning 68% System design tasks
Documentation Generation 75% API docs, README files
Configuration Optimization 78% Nginx, PostgreSQL, etc.
Error Analysis 81% Log analysis, stack traces

Note: Accuracy is lower than larger cloud models (GPT-3.5, GPT-4) but acceptable for on-device use with zero API costs.

Resource Usage

Memory Usage

Component Base Memory Per-Request Overhead
AI Engine 200MB 10MB
HTTP Server 25MB 2MB
CLI Tool 15MB N/A
Kernel Modules 5MB 1MB
Total (idle) 245MB -
Total (active) 245MB +13MB per request

CPU Usage

Operation CPU Usage (single core) CPU Usage (4 cores)
Idle 2% 0.5% per core
Simple Query 45% 12% per core
Complex Query 85% 22% per core
Batch Processing 95% 25% per core

Disk I/O

  • Model Loading: 350MB read (one-time, on startup)
  • Per-Request: <1MB (minimal, mostly memory-based)
  • Logging: ~10KB per request (if enabled)

Scalability

Vertical Scaling

CPU Cores Throughput (req/sec) Improvement
1 6.2 Baseline
2 11.8 1.9x
4 18.5 3.0x
8 28.3 4.6x
16 35.1 5.7x

Note: Diminishing returns due to memory bandwidth limitations.

Horizontal Scaling

Multiple Cortex Linux instances can be load-balanced:

# Nginx load balancer configuration
upstream cortex_ai {
    least_conn;
    server cortex1:8080;
    server cortex2:8080;
    server cortex3:8080;
}

server {
    listen 80;
    location / {
        proxy_pass http://cortex_ai;
    }
}

Scaling Characteristics:

  • Linear scaling up to ~10 instances
  • Network overhead: Minimal (local network)
  • Coordination: Stateless (no shared state)

Hardware Requirements

Minimum Requirements

Component Specification
CPU x86_64 or ARM64, 2 cores, 2.0 GHz
RAM 2GB (4GB recommended)
Storage 10GB free space (20GB recommended)
Network Optional (for updates only)

Recommended Requirements

Component Specification
CPU 4+ cores, 3.0+ GHz, modern architecture
RAM 8GB+
Storage 50GB+ SSD
Network Broadband (for updates)

Production Requirements

Component Specification
CPU 8+ cores, 3.5+ GHz, latest generation
RAM 16GB+
Storage 100GB+ NVMe SSD
Network Gigabit Ethernet
Redundancy Multiple instances for HA

Cloud Instance Recommendations

AWS EC2

  • Minimum: t3.medium (2 vCPU, 4GB RAM)
  • Recommended: t3.large (2 vCPU, 8GB RAM)
  • Production: t3.xlarge (4 vCPU, 16GB RAM) or larger

DigitalOcean

  • Minimum: s-2vcpu-4gb ($24/month)
  • Recommended: s-4vcpu-8gb ($48/month)
  • Production: s-8vcpu-16gb ($96/month)

Google Cloud

  • Minimum: n1-standard-2 (2 vCPU, 7.5GB RAM)
  • Recommended: n1-standard-4 (4 vCPU, 15GB RAM)
  • Production: n1-standard-8 (8 vCPU, 30GB RAM)

Software Stack

Operating System

  • Base: Debian 12 (Bookworm) / Ubuntu 22.04 LTS
  • Kernel: Linux 6.1.0+ (Cortex-enhanced)
  • Init System: systemd 251+

Core Libraries

Library Version Purpose
glibc 2.35+ C standard library
OpenSSL 3.0+ Cryptography
zlib 1.2.13+ Compression
libcurl 7.85+ HTTP client (optional)
libyaml 0.2.5+ Configuration parsing

AI Engine Dependencies

Dependency Version Purpose
Eigen 3.4+ Linear algebra
ONNX Runtime 1.15+ Model inference (optional)
BLAS OpenBLAS 0.3.20+ Matrix operations

Development Tools

Tool Version Purpose
GCC 11+ C/C++ compiler
Clang 14+ Alternative C/C++ compiler
Rust 1.70+ CLI tool development
Go 1.20+ HTTP API server
Python 3.10+ SDK and tooling
CMake 3.20+ Build system

Runtime Dependencies

Package Version Purpose
systemd 251+ Service management
dbus 1.14+ Inter-process communication
libsystemd 251+ systemd integration

Kernel Enhancements

AI-Aware Process Scheduler

Module: cortex_scheduler.ko

Features:

  • Priority boost for AI inference tasks
  • CPU affinity optimization
  • Real-time scheduling support
  • Workload-aware CPU allocation

Configuration:

# Enable AI-aware scheduling
echo 1 > /sys/kernel/cortex/scheduler/enabled

# Set AI process priority
echo 10 > /sys/kernel/cortex/scheduler/ai_priority

# Configure CPU affinity
echo "0-3" > /sys/kernel/cortex/scheduler/ai_cpus

Enhanced Memory Management

Module: cortex_memory.ko

Features:

  • Large page support for model memory
  • Memory compaction for AI workloads
  • NUMA awareness
  • Memory pressure handling

Configuration:

# Enable large pages
echo 1 > /sys/kernel/cortex/memory/large_pages

# Set memory limits
echo 512 > /sys/kernel/cortex/memory/max_mb

System Monitoring

Module: cortex_monitor.ko

Features:

  • Real-time performance metrics
  • Resource usage tracking
  • Event logging
  • Performance counters

Metrics Exposed:

  • /proc/cortex/performance: Performance counters
  • /proc/cortex/memory: Memory usage
  • /proc/cortex/cpu: CPU usage
  • /proc/cortex/requests: Request statistics

AI Engine Specifications

Model Details

Name: Sapiens 0.27B

Architecture:

  • Type: Decoder-only transformer
  • Parameters: 270,000,000
  • Layers: 24
  • Attention Heads: 16
  • Hidden Dimension: 1024
  • Intermediate Dimension: 4096
  • Vocabulary Size: 50,257
  • Context Length: 2048 tokens
  • Position Encoding: Rotary Position Embedding (RoPE)

Quantization:

  • Format: INT8 quantization
  • Calibration: Per-channel quantization
  • Accuracy Loss: <2% compared to FP16

Optimizations:

  • Operator Fusion: Fused attention and MLP operations
  • Kernel Optimization: SIMD-optimized kernels
  • Memory Layout: Optimized for cache locality
  • Batch Processing: Efficient batching support

Inference Engine

Language: C++17

Key Components:

  • Tokenizer: Fast BPE tokenization
  • Embedding Layer: Efficient embedding lookup
  • Transformer Blocks: Optimized attention and MLP
  • Output Layer: Language modeling head

Performance Optimizations:

  • SIMD: AVX2/AVX-512 vectorization
  • Multi-threading: OpenMP parallelization
  • Memory Pool: Pre-allocated memory pools
  • Caching: KV cache for repeated queries

API Interface

C API: See Developer Documentation - C API

Python API: See AI Integration - Python Integration

Performance Tuning

CPU Tuning

# Set CPU governor to performance
echo performance | sudo tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor

# Disable CPU frequency scaling
sudo cpupower frequency-set -g performance

# Set CPU affinity
taskset -cp 0-3 $(pgrep cortex-ai)

Memory Tuning

# /etc/cortex-ai/config.yaml
ai:
  # Increase memory allocation
  max_memory_mb: 1024
  
  # Enable memory pooling
  enable_memory_pool: true
  memory_pool_size_mb: 512

I/O Tuning

# Set I/O scheduler
echo none > /sys/block/nvme0n1/queue/scheduler

# Increase read-ahead
sudo blockdev --setra 8192 /dev/nvme0n1

Network Tuning (for HTTP API)

# Increase TCP buffer sizes
echo 'net.core.rmem_max = 16777216' | sudo tee -a /etc/sysctl.conf
echo 'net.core.wmem_max = 16777216' | sudo tee -a /etc/sysctl.conf
sudo sysctl -p

Model-Specific Tuning

# /etc/cortex-ai/config.yaml
ai:
  # Thread configuration
  num_threads: 4  # Match CPU cores
  
  # Batch processing
  batch_size: 10  # Increase for throughput
  
  # Generation parameters
  temperature: 0.7
  top_p: 0.9
  top_k: 40
  max_tokens: 512

Scalability

Vertical Scaling

Increase resources on a single instance:

  • CPU: Add more cores (linear scaling up to ~8 cores)
  • RAM: Increase memory (allows larger batches)
  • Storage: Use faster storage (NVMe SSD)

Horizontal Scaling

Deploy multiple instances behind a load balancer:

# Load balancer configuration
services:
  cortex1:
    image: cortex-linux:latest
    ports:
      - "8081:8080"
  
  cortex2:
    image: cortex-linux:latest
    ports:
      - "8082:8080"
  
  cortex3:
    image: cortex-linux:latest
    ports:
      - "8083:8080"
  
  nginx:
    image: nginx:latest
    volumes:
      - ./nginx.conf:/etc/nginx/nginx.conf
    ports:
      - "8080:80"

Caching Strategy

# Enable caching
caching:
  enabled: true
  backend: redis  # or memory
  ttl_seconds: 3600
  max_size_mb: 1000

Monitoring and Metrics

# Enable Prometheus metrics
# /etc/cortex-ai/config.yaml
monitoring:
  prometheus:
    enabled: true
    port: 9090
    path: /metrics

Next Steps

Last updated: 2024

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